Cumulant method based on Latin hypercube sampling for calculating probabilistic power flow

被引：0
|
作者：
Huang Y. [1 ]
Xu Q. [1 ]
Bian H. [2 ]
Liu J. [3 ]
机构：
[1] School of Electrical Engineering, Southeast University, Nanjing
[2] School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing
[3] Jiangsu Electric Power Research Institute, Nanjing
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2016年 / 36卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Cholesky decomposition; Correlation; Cumulant method; Latin hypercube sampling; Multi-linearization; Probabilistic power flow;
D O I：
10.16081/j.issn.1006-6047.2016.11.017
中图分类号：
学科分类号：
摘要：
As the development of new-energy power generation increases the operational uncertainty and correlation of power system, the traditional power flow calculation algorithm could not meet the requirements of power grid analysis and evaluation in this environment. A probabilistic power flow calculation algorithm is proposed, which applies the cumulant method based on Latin hypercube sampling to consider the correlation between input variables. It combines the high precision and wide applicability of analog method with the high speed of cumulant method, adopts the multi-linearized power flow model to reduce the truncation error, and applies Cholesky decomposition to solve the problem that the cumulant method can only deal with the independent variables. The simulative results of IEEE 30-bus system and IEEE 118-bus system verify the accuracy, rapidity and practicability of the proposed algorithm. © 2016, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码：112 / 119
页数：7
相关论文
共 28 条
  • [1] Dong Z., Zhao J., Wen F., Et al., From smart grid to energy internet: basic concepts and research framework, Automation of Electric Power Systems, 38, 15, pp. 1-11, (2014)
  • [2] Zhao H., Cai W., Wang J., Et al., Energy internet architecture design and topology model, Transactions of China Electrotechnical Society, 30, 11, pp. 30-36, (2015)
  • [3] Allan R.N., Al-Shakarchi M.R.G., Probabilistic techniques in AC load-flow analysis, Proceedings of the Institution of Electrical Engineers, 124, 2, pp. 154-160, (1977)
  • [4] Xie K., Roy B., Considering wind speed correlation of WECS in reliability evaluation using the time-shifting technique, Electric Power System Research, 79, 4, pp. 687-693, (2009)
  • [5] Fan R., Chen J., Duan X., Et al., Impact of wind speed correlation on probabilistic load flow, Automation of Electric Power Systems, 35, 4, pp. 18-22, (2011)
  • [6] Ding M., Li S., Hong M., The K-means cluster based load model for power system probabilistic analysis, Automation of Electric Power Systems, 23, 19, pp. 51-54, (1999)
  • [7] Peng H., Cao Y., Huang X., Et al., Probabilistic load flow calculation based on combination sampling for power system containing distributed generations, Electric Power Automation Equipment, 34, 5, pp. 28-34, (2014)
  • [8] Caramia P., Garpinelli G., Varilone P., Point estimate schemes for probabilistic three-phase load flow, Electric Power System Research, 80, 2, pp. 168-175, (2010)
  • [9] Allen R.N., Leite De S.A.M., Burchett R.C., Evaluation methods and accuracy in probabilistic load flow solutions, IEEE Transactions on Power Apparatus and Systems, 100, 5, pp. 2539-2546, (1981)
  • [10] Guo X., Cai D., Comparison of probabilistic load flow calculation based on cumulant method among different series expansions, Electric Power Automation Equipment, 33, 12, pp. 85-90, (2013)
123